Lung cancer is the number one cancer killer in the US for both men and women. Almost 156,000 (85%) of patients who present with lung cancer die from the disease within 5 years of presentation. There are several reasons for these stark mortality figures including non-existing screening, and ineffective therapeutic options for most patients. Even patients with stage I lung cancer who are treated surgically with intent to cure have a 30% five-year mortality due to metastatic disease. Early efforts to screen patients with spiral chest CT are being undertaken in the US but are limited by the large number of benign nodules discovered. We recently described a new method to translate data obtained from gene profiling with microarrays into simple clinically usable tests utilizing gene ratios. In preliminary work we developed models to detect lung cancer in minute specimens of lung nodules, and distinguish between different types of cancers that may affect the lung. We also developed a model to predict patient outcome after surgery for early lung cancer in a way that adds to current staging techniques. All of these tests utilize gene expression profiling data that can be acquired with microarrays or quantitative RT-PCR. We propose to extend our preliminary findings in order to determine if our gene ratio tests can 1. Distinguish between lung adenocarcinoma, mesothelioma and adenocarcinoma metastatic from breast origin to the lung using tumor bank specimens. 2. Accurately predict outcome using a set of specimens obtained from patients who underwent surgery for lung adenocarcinoma and linked with outcomes database. 3. Allow detection of lung cancer using fine needle aspirations obtained from lung nodules. We envision that our methodology will be adaptable to a future clinical scenario where each suspicious nodule would be evaluated by fine needle aspiration determining the diagnosis and best therapeutic options for the patients. [unreadable] [unreadable]